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Adding SegGPT (#27735)
* First commit * Improvements * More improvements * Converted original checkpoint to HF checkpoint * Fix style * Fixed forward * More improvements * More improvements * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Remove asserts * Remove unnecessary attributes * Changed model name to camel case * Improve forward doc * Improve tests * More improvements * Fix copies * Fix doc * Make SegGptImageProcessor more flexible * Added few-shot test * Fix style * Update READMEs and docs * Update READMEs * Make inputs required * Add SegGptForImageSegmentation * Make tests pass * Rename to out_indicies * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Fixed naming convention * Copying SegGptMlp from modeling_sam.py * Some minor improvements * Remove mlp_ratio * Fix docstrings * Fixed docstring match * Objects defined before use * Storing only patch_size and beta for SegGptLoss * removed _prepare_inputs method * Removed modified from headers * Renamed to output_indicies * Removed unnecessary einsums * Update tests/models/seggpt/test_modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/seggpt/test_modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/seggpt/test_modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fixing issues * Raise error as soon as possible * More fixes * Fix merge * Added palette to SegGptImageProcessor * Fixed typo * Fixed shape typo * Added permute before doing palette to class mapping * Fixed style * Fixed and added tests * Fixed docstrings * Matching SegFormer API for post_processing_semantic_segmentation * Fixed copies * Fixed SegGptImageProcessor to handle both binary and RGB masks * Updated docstrings of SegGptImageProcessor * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update docs/source/en/model_doc/seggpt.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/configuration_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/convert_seggpt_to_hf.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/seggpt/test_image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/seggpt/test_modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Object definitions above & fix style * Renamed output_indices to intermediate_feature_indices * Removed unnecessary check on bool_masked_pos * Loss first in the outputs * Added validation for do_normalize * Improved SegGptImageProcessor and added new tests * Added comment * Added docstrings to SegGptLoss * Reimplemented ensemble condition logic in SegGptEncoder * Update src/transformers/models/seggpt/__init__.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/seggpt/convert_seggpt_to_hf.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/seggpt/configuration_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Updated docstrings to use post_process_semantic_segmentation * Fixed typo on docstrings * moved pixel values test to test_image_processing_seggpt * Addressed comments * Update src/transformers/models/seggpt/configuration_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/image_processing_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/configuration_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Updated docstrings for SegGptLoss * Address comments * Added SegGpt example to model docs * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * moved patchify and unpatchify * Rename checkpoint * Renamed intermediate_features to intermediate_hidden_states for consistency * Update src/transformers/models/seggpt/configuration_seggpt.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Replaced post_process_masks for post_process_semantic_segmentation in the docs --------- Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Niels <niels.rogge1@gmail.com> Co-authored-by: Eduardo Pacheco <eduardo.pacheco@limehome.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Eduardo Pacheco
GitHub
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3fcfbe7549
@ -482,6 +482,7 @@ Current number of checkpoints: ** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (from Beijing Academy of Artificial Intelligence (BAAI)) released with the paper [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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@ -455,6 +455,7 @@ Número actual de puntos de control: ** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (from Beijing Academy of Artificial Intelligence (BAAI) released with the paper [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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@ -476,6 +476,7 @@ Nombre actuel de points de contrôle : ** (de Meta AI) a été publié dans l'article [SeamlessM4T — Traduction multimodale et massivement multilingue](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) par l'équipe de communication transparente.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (de Meta AI) a été publié dans l'article [Seamless: Traduction de la parole multilingue, expressive et en continu](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) par l'équipe de communication transparente.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (de NVIDIA) a été publié dans l'article [SegFormer : Conception simple et efficace pour la segmentation sémantique avec des transformateurs](https://arxiv.org/abs/2105.15203) par Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (de Beijing Academy of Artificial Intelligence (BAAI) publié dans l'article [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) parXinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (de Meta AI) a été publié dans l'article [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) par Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (de ASAPP) a été publié dans l'article [Compromis entre performances et efficacité dans l'entraînement non supervisé pour la reconnaissance vocale](https://arxiv.org/abs/2109.06870) par Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (de ASAPP) a été publié dans l'article [Compromis entre performances et efficacité dans l'entraînement non supervisé pour la reconnaissance vocale](https://arxiv.org/abs/2109.06870) par Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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@ -429,6 +429,7 @@ conda install conda-forge::transformers
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1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (Beijing Academy of Artificial Intelligence (BAAI से) Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. द्वाराअनुसंधान पत्र [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) के साथ जारी किया गया
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI से) Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick. द्वाराअनुसंधान पत्र [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) के साथ जारी किया गया
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP से) साथ देने वाला पेपर [भाषण पहचान के लिए अनसुपरवाइज्ड प्री-ट्रेनिंग में परफॉर्मेंस-एफिशिएंसी ट्रेड-ऑफ्स](https://arxiv.org/abs/2109.06870) फेलिक्स वू, क्वांगयुन किम, जिंग पैन, क्यू हान, किलियन क्यू. वेनबर्गर, योव आर्टज़ी द्वारा।
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP से) साथ में पेपर [भाषण पहचान के लिए अनसुपरवाइज्ड प्री-ट्रेनिंग में परफॉर्मेंस-एफिशिएंसी ट्रेड-ऑफ्स](https://arxiv.org/abs/2109.06870) फेलिक्स वू, क्वांगयुन किम, जिंग पैन, क्यू हान, किलियन क्यू. वेनबर्गर, योआव आर्टज़ी द्वारा पोस्ट किया गया।
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@ -489,6 +489,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
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1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (NVIDIA から) Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo から公開された研究論文: [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (Beijing Academy of Artificial Intelligence (BAAI から) Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. から公開された研究論文 [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284)
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI から) Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick. から公開された研究論文 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP から) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi から公開された研究論文: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP から) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi から公開された研究論文: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
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@ -404,6 +404,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
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1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
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1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
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1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (NVIDIA 에서) Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 의 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 논문과 함께 발표했습니다.
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1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (Beijing Academy of Artificial Intelligence (BAAI 에서 제공)은 Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.의 [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284)논문과 함께 발표했습니다.
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI 에서 제공)은 Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.의 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)논문과 함께 발표했습니다.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP 에서) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 의 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 논문과 함께 발표했습니다.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP 에서) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 의 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 논문과 함께 발표했습니다.
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@ -428,6 +428,7 @@ conda install conda-forge::transformers
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1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
|
||||
1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (来自 NVIDIA) 伴随论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 由 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 发布。
|
||||
1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (来自 Beijing Academy of Artificial Intelligence (BAAI) 伴随论文 [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) 由 Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang 发布。
|
||||
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (来自 Meta AI) 伴随论文 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) 由 Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick 发布。
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
|
||||
|
||||
@ -440,6 +440,7 @@ conda install conda-forge::transformers
|
||||
1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
|
||||
1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
|
||||
1. **[SegGPT](https://huggingface.co/docs/transformers/main/model_doc/seggpt)** (from Beijing Academy of Artificial Intelligence (BAAI) released with the paper [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.
|
||||
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
|
||||
@ -583,6 +583,8 @@
|
||||
title: ResNet
|
||||
- local: model_doc/segformer
|
||||
title: SegFormer
|
||||
- local: model_doc/seggpt
|
||||
title: SegGpt
|
||||
- local: model_doc/swiftformer
|
||||
title: SwiftFormer
|
||||
- local: model_doc/swin
|
||||
|
||||
@ -251,6 +251,7 @@ Flax), PyTorch, and/or TensorFlow.
|
||||
| [SeamlessM4T](model_doc/seamless_m4t) | ✅ | ❌ | ❌ |
|
||||
| [SeamlessM4Tv2](model_doc/seamless_m4t_v2) | ✅ | ❌ | ❌ |
|
||||
| [SegFormer](model_doc/segformer) | ✅ | ✅ | ❌ |
|
||||
| [SegGPT](model_doc/seggpt) | ✅ | ❌ | ❌ |
|
||||
| [SEW](model_doc/sew) | ✅ | ❌ | ❌ |
|
||||
| [SEW-D](model_doc/sew-d) | ✅ | ❌ | ❌ |
|
||||
| [SigLIP](model_doc/siglip) | ✅ | ❌ | ❌ |
|
||||
|
||||
90
docs/source/en/model_doc/seggpt.md
Normal file
90
docs/source/en/model_doc/seggpt.md
Normal file
@ -0,0 +1,90 @@
|
||||
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||||
rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# SegGPT
|
||||
|
||||
## Overview
|
||||
|
||||
The SegGPT model was proposed in [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. SegGPT employs a decoder-only Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of*
|
||||
|
||||
Tips:
|
||||
- One can use [`SegGptImageProcessor`] to prepare image input, prompt and mask to the model.
|
||||
- It's highly advisable to pass `num_labels` (not considering background) during preprocessing and postprocessing with [`SegGptImageProcessor`] for your use case.
|
||||
- When doing infenrece with [`SegGptForImageSegmentation`] if your `batch_size` is greater than 1 you can use feature ensemble across your images by passing `feature_ensemble=True` in the forward method.
|
||||
|
||||
Here's how to use the model for one-shot semantic segmentation:
|
||||
|
||||
```python
|
||||
import torch
|
||||
from datasets import load_dataset
|
||||
from transformers import SegGptImageProcessor, SegGptForImageSegmentation
|
||||
|
||||
model_id = "BAAI/seggpt-vit-large"
|
||||
image_processor = SegGptImageProcessor.from_pretrained(checkpoint)
|
||||
model = SegGptForImageSegmentation.from_pretrained(checkpoint)
|
||||
|
||||
dataset_id = "EduardoPacheco/FoodSeg103"
|
||||
ds = load_dataset(dataset_id, split="train")
|
||||
# Number of labels in FoodSeg103 (not including background)
|
||||
num_labels = 103
|
||||
|
||||
image_input = ds[4]["image"]
|
||||
ground_truth = ds[4]["label"]
|
||||
image_prompt = ds[29]["image"]
|
||||
mask_prompt = ds[29]["label"]
|
||||
|
||||
inputs = image_processor(
|
||||
images=image_input,
|
||||
prompt_images=image_prompt,
|
||||
prompt_masks=mask_prompt,
|
||||
num_labels=num_labels,
|
||||
return_tensors="pt"
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
target_sizes = [image_input.size[::-1]]
|
||||
mask = image_processor.post_process_semantic_segmentation(outputs, target_sizes, num_labels=num_labels)[0]
|
||||
```
|
||||
|
||||
This model was contributed by [EduardoPacheco](https://huggingface.co/EduardoPacheco).
|
||||
The original code can be found [here]([(https://github.com/baaivision/Painter/tree/main)).
|
||||
|
||||
|
||||
## SegGptConfig
|
||||
|
||||
[[autodoc]] SegGptConfig
|
||||
|
||||
## SegGptImageProcessor
|
||||
|
||||
[[autodoc]] SegGptImageProcessor
|
||||
- preprocess
|
||||
- post_process_semantic_segmentation
|
||||
|
||||
## SegGptModel
|
||||
|
||||
[[autodoc]] SegGptModel
|
||||
- forward
|
||||
|
||||
## SegGptForImageSegmentation
|
||||
|
||||
[[autodoc]] SegGptForImageSegmentation
|
||||
- forward
|
||||
@ -767,6 +767,7 @@ _import_structure = {
|
||||
"SeamlessM4Tv2Config",
|
||||
],
|
||||
"models.segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"],
|
||||
"models.seggpt": ["SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegGptConfig"],
|
||||
"models.sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"],
|
||||
"models.sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"],
|
||||
"models.siglip": [
|
||||
@ -1316,6 +1317,7 @@ else:
|
||||
_import_structure["models.pvt"].extend(["PvtImageProcessor"])
|
||||
_import_structure["models.sam"].extend(["SamImageProcessor"])
|
||||
_import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"])
|
||||
_import_structure["models.seggpt"].extend(["SegGptImageProcessor"])
|
||||
_import_structure["models.siglip"].append("SiglipImageProcessor")
|
||||
_import_structure["models.swin2sr"].append("Swin2SRImageProcessor")
|
||||
_import_structure["models.tvlt"].append("TvltImageProcessor")
|
||||
@ -3192,6 +3194,14 @@ else:
|
||||
"SegformerPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.seggpt"].extend(
|
||||
[
|
||||
"SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"SegGptForImageSegmentation",
|
||||
"SegGptModel",
|
||||
"SegGptPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.sew"].extend(
|
||||
[
|
||||
"SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
@ -5531,10 +5541,8 @@ if TYPE_CHECKING:
|
||||
SEAMLESS_M4T_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
SeamlessM4Tv2Config,
|
||||
)
|
||||
from .models.segformer import (
|
||||
SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
SegformerConfig,
|
||||
)
|
||||
from .models.segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig
|
||||
from .models.seggpt import SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, SegGptConfig
|
||||
from .models.sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
|
||||
from .models.sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
|
||||
from .models.siglip import (
|
||||
@ -6080,6 +6088,7 @@ if TYPE_CHECKING:
|
||||
from .models.pvt import PvtImageProcessor
|
||||
from .models.sam import SamImageProcessor
|
||||
from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor
|
||||
from .models.seggpt import SegGptImageProcessor
|
||||
from .models.siglip import SiglipImageProcessor
|
||||
from .models.swin2sr import Swin2SRImageProcessor
|
||||
from .models.tvlt import TvltImageProcessor
|
||||
@ -7635,6 +7644,12 @@ if TYPE_CHECKING:
|
||||
SegformerModel,
|
||||
SegformerPreTrainedModel,
|
||||
)
|
||||
from .models.seggpt import (
|
||||
SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SegGptForImageSegmentation,
|
||||
SegGptModel,
|
||||
SegGptPreTrainedModel,
|
||||
)
|
||||
from .models.sew import (
|
||||
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SEWForCTC,
|
||||
|
||||
@ -194,6 +194,7 @@ from . import (
|
||||
seamless_m4t,
|
||||
seamless_m4t_v2,
|
||||
segformer,
|
||||
seggpt,
|
||||
sew,
|
||||
sew_d,
|
||||
siglip,
|
||||
|
||||
@ -202,6 +202,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
||||
("seamless_m4t", "SeamlessM4TConfig"),
|
||||
("seamless_m4t_v2", "SeamlessM4Tv2Config"),
|
||||
("segformer", "SegformerConfig"),
|
||||
("seggpt", "SegGptConfig"),
|
||||
("sew", "SEWConfig"),
|
||||
("sew-d", "SEWDConfig"),
|
||||
("siglip", "SiglipConfig"),
|
||||
@ -428,6 +429,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
|
||||
("seamless_m4t", "SEAMLESS_M4T_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("seamless_m4t_v2", "SEAMLESS_M4T_V2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("segformer", "SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("seggpt", "SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("sew", "SEW_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("sew-d", "SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("siglip", "SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
@ -680,6 +682,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
||||
("seamless_m4t", "SeamlessM4T"),
|
||||
("seamless_m4t_v2", "SeamlessM4Tv2"),
|
||||
("segformer", "SegFormer"),
|
||||
("seggpt", "SegGPT"),
|
||||
("sew", "SEW"),
|
||||
("sew-d", "SEW-D"),
|
||||
("siglip", "SigLIP"),
|
||||
|
||||
@ -98,6 +98,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
|
||||
("resnet", "ConvNextImageProcessor"),
|
||||
("sam", "SamImageProcessor"),
|
||||
("segformer", "SegformerImageProcessor"),
|
||||
("seggpt", "SegGptImageProcessor"),
|
||||
("siglip", "SiglipImageProcessor"),
|
||||
("swiftformer", "ViTImageProcessor"),
|
||||
("swin", "ViTImageProcessor"),
|
||||
|
||||
@ -193,6 +193,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
||||
("seamless_m4t", "SeamlessM4TModel"),
|
||||
("seamless_m4t_v2", "SeamlessM4Tv2Model"),
|
||||
("segformer", "SegformerModel"),
|
||||
("seggpt", "SegGptModel"),
|
||||
("sew", "SEWModel"),
|
||||
("sew-d", "SEWDModel"),
|
||||
("siglip", "SiglipModel"),
|
||||
|
||||
71
src/transformers/models/seggpt/__init__.py
Normal file
71
src/transformers/models/seggpt/__init__.py
Normal file
@ -0,0 +1,71 @@
|
||||
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
|
||||
|
||||
|
||||
_import_structure = {
|
||||
"configuration_seggpt": ["SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegGptConfig", "SegGptOnnxConfig"]
|
||||
}
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["modeling_seggpt"] = [
|
||||
"SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"SegGptModel",
|
||||
"SegGptPreTrainedModel",
|
||||
"SegGptForImageSegmentation",
|
||||
]
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["image_processing_seggpt"] = ["SegGptImageProcessor"]
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_seggpt import SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, SegGptConfig, SegGptOnnxConfig
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .modeling_seggpt import (
|
||||
SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SegGptForImageSegmentation,
|
||||
SegGptModel,
|
||||
SegGptPreTrainedModel,
|
||||
)
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .image_processing_seggpt import SegGptImageProcessor
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
||||
145
src/transformers/models/seggpt/configuration_seggpt.py
Normal file
145
src/transformers/models/seggpt/configuration_seggpt.py
Normal file
@ -0,0 +1,145 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" SegGpt model configuration"""
|
||||
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"BAAI/seggpt-vit-large": "https://huggingface.co/BAAI/seggpt-vit-large/resolve/main/config.json",
|
||||
}
|
||||
|
||||
|
||||
class SegGptConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the SegGPT
|
||||
[BAAI/seggpt-vit-large](https://huggingface.co/BAAI/seggpt-vit-large) architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
hidden_size (`int`, *optional*, defaults to 1024):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 24):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 16):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||
`"relu"`, `"selu"` and `"gelu_new"` are supported.
|
||||
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
|
||||
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the layer normalization layers.
|
||||
image_size (`List[int]`, *optional*, defaults to `[896, 448]`):
|
||||
The size (resolution) of each image.
|
||||
patch_size (`int`, *optional*, defaults to 16):
|
||||
The size (resolution) of each patch.
|
||||
num_channels (`int`, *optional*, defaults to 3):
|
||||
The number of input channels.
|
||||
qkv_bias (`bool`, *optional*, defaults to `True`):
|
||||
Whether to add a bias to the queries, keys and values.
|
||||
mlp_dim (`int`, *optional*):
|
||||
The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to
|
||||
`hidden_size` * 4.
|
||||
drop_path_rate (`float`, *optional*, defaults to 0.1):
|
||||
The drop path rate for the dropout layers.
|
||||
pretrain_image_size (`int`, *optional*, defaults to 224):
|
||||
The pretrained size of the absolute position embeddings.
|
||||
decoder_hidden_size (`int`, *optional*, defaults to 64):
|
||||
Hidden size for decoder.
|
||||
use_relative_position_embeddings (`bool`, *optional*, defaults to `True`):
|
||||
Whether to use relative position embeddings in the attention layers.
|
||||
merge_index (`int`, *optional*, defaults to 2):
|
||||
The index of the encoder layer to merge the embeddings.
|
||||
intermediate_hidden_state_indices (`List[int]`, *optional*, defaults to `[5, 11, 17, 23]`):
|
||||
The indices of the encoder layers which we store as features for the decoder.
|
||||
beta (`float`, *optional*, defaults to 0.01):
|
||||
Regularization factor for SegGptLoss (smooth-l1 loss).
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import SegGptConfig, SegGptModel
|
||||
|
||||
>>> # Initializing a SegGPT seggpt-vit-large style configuration
|
||||
>>> configuration = SegGptConfig()
|
||||
|
||||
>>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration
|
||||
>>> model = SegGptModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "seggpt"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=1024,
|
||||
num_hidden_layers=24,
|
||||
num_attention_heads=16,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout_prob=0.0,
|
||||
initializer_range=0.02,
|
||||
layer_norm_eps=1e-6,
|
||||
image_size=[896, 448],
|
||||
patch_size=16,
|
||||
num_channels=3,
|
||||
qkv_bias=True,
|
||||
mlp_dim=None,
|
||||
drop_path_rate=0.1,
|
||||
pretrain_image_size=224,
|
||||
decoder_hidden_size=64,
|
||||
use_relative_position_embeddings=True,
|
||||
merge_index=2,
|
||||
intermediate_hidden_state_indices=[5, 11, 17, 23],
|
||||
beta=0.01,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
if merge_index > min(intermediate_hidden_state_indices):
|
||||
raise ValueError(
|
||||
f"Merge index must be less than the minimum encoder output index, but got {merge_index=} and {intermediate_hidden_state_indices=}"
|
||||
)
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.initializer_range = initializer_range
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.num_channels = num_channels
|
||||
self.qkv_bias = qkv_bias
|
||||
self.drop_path_rate = drop_path_rate
|
||||
self.pretrain_image_size = pretrain_image_size
|
||||
self.decoder_hidden_size = decoder_hidden_size
|
||||
self.use_relative_position_embeddings = use_relative_position_embeddings
|
||||
self.merge_index = merge_index
|
||||
self.intermediate_hidden_state_indices = intermediate_hidden_state_indices
|
||||
self.beta = beta
|
||||
self.mlp_dim = int(hidden_size * 4) if mlp_dim is None else mlp_dim
|
||||
222
src/transformers/models/seggpt/convert_seggpt_to_hf.py
Normal file
222
src/transformers/models/seggpt/convert_seggpt_to_hf.py
Normal file
@ -0,0 +1,222 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Convert SegGPT checkpoints from the original repository.
|
||||
|
||||
URL: https://github.com/baaivision/Painter/tree/main/SegGPT
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
|
||||
import requests
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
from transformers import SegGptConfig, SegGptForImageSegmentation, SegGptImageProcessor
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# here we list all keys to be renamed (original name on the left, our name on the right)
|
||||
def create_rename_keys(config):
|
||||
rename_keys = []
|
||||
|
||||
# fmt: off
|
||||
|
||||
# rename embedding and its parameters
|
||||
rename_keys.append(("patch_embed.proj.weight", "model.embeddings.patch_embeddings.projection.weight"))
|
||||
rename_keys.append(("patch_embed.proj.bias", "model.embeddings.patch_embeddings.projection.bias"))
|
||||
rename_keys.append(("mask_token", "model.embeddings.mask_token"))
|
||||
rename_keys.append(("segment_token_x", "model.embeddings.segment_token_input"))
|
||||
rename_keys.append(("segment_token_y", "model.embeddings.segment_token_prompt"))
|
||||
rename_keys.append(("type_token_cls", "model.embeddings.type_token_semantic"))
|
||||
rename_keys.append(("type_token_ins", "model.embeddings.type_token_instance"))
|
||||
rename_keys.append(("pos_embed", "model.embeddings.position_embeddings"))
|
||||
|
||||
# rename decoder and other
|
||||
rename_keys.append(("norm.weight", "model.encoder.layernorm.weight"))
|
||||
rename_keys.append(("norm.bias", "model.encoder.layernorm.bias"))
|
||||
rename_keys.append(("decoder_embed.weight", "decoder.decoder_embed.weight"))
|
||||
rename_keys.append(("decoder_embed.bias", "decoder.decoder_embed.bias"))
|
||||
rename_keys.append(("decoder_pred.0.weight", "decoder.decoder_pred.conv.weight"))
|
||||
rename_keys.append(("decoder_pred.0.bias", "decoder.decoder_pred.conv.bias"))
|
||||
rename_keys.append(("decoder_pred.1.weight", "decoder.decoder_pred.layernorm.weight"))
|
||||
rename_keys.append(("decoder_pred.1.bias", "decoder.decoder_pred.layernorm.bias"))
|
||||
rename_keys.append(("decoder_pred.3.weight", "decoder.decoder_pred.head.weight"))
|
||||
rename_keys.append(("decoder_pred.3.bias", "decoder.decoder_pred.head.bias"))
|
||||
|
||||
# rename blocks
|
||||
for i in range(config.num_hidden_layers):
|
||||
rename_keys.append((f"blocks.{i}.attn.qkv.weight", f"model.encoder.layers.{i}.attention.qkv.weight"))
|
||||
rename_keys.append((f"blocks.{i}.attn.qkv.bias", f"model.encoder.layers.{i}.attention.qkv.bias"))
|
||||
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"model.encoder.layers.{i}.attention.proj.weight"))
|
||||
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"model.encoder.layers.{i}.attention.proj.bias"))
|
||||
rename_keys.append((f"blocks.{i}.attn.rel_pos_h", f"model.encoder.layers.{i}.attention.rel_pos_h"))
|
||||
rename_keys.append((f"blocks.{i}.attn.rel_pos_w", f"model.encoder.layers.{i}.attention.rel_pos_w"))
|
||||
|
||||
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"model.encoder.layers.{i}.mlp.lin1.weight"))
|
||||
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"model.encoder.layers.{i}.mlp.lin1.bias"))
|
||||
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"model.encoder.layers.{i}.mlp.lin2.weight"))
|
||||
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"model.encoder.layers.{i}.mlp.lin2.bias"))
|
||||
|
||||
rename_keys.append((f"blocks.{i}.norm1.weight", f"model.encoder.layers.{i}.layernorm_before.weight"))
|
||||
rename_keys.append((f"blocks.{i}.norm1.bias", f"model.encoder.layers.{i}.layernorm_before.bias"))
|
||||
rename_keys.append((f"blocks.{i}.norm2.weight", f"model.encoder.layers.{i}.layernorm_after.weight"))
|
||||
rename_keys.append((f"blocks.{i}.norm2.bias", f"model.encoder.layers.{i}.layernorm_after.bias"))
|
||||
|
||||
# fmt: on
|
||||
|
||||
return rename_keys
|
||||
|
||||
|
||||
def rename_key(dct, old, new):
|
||||
val = dct.pop(old)
|
||||
dct[new] = val
|
||||
|
||||
|
||||
# We will verify our results on spongebob images
|
||||
def prepare_input():
|
||||
image_input_url = (
|
||||
"https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg"
|
||||
)
|
||||
image_prompt_url = (
|
||||
"https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg"
|
||||
)
|
||||
mask_prompt_url = (
|
||||
"https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png"
|
||||
)
|
||||
|
||||
image_input = Image.open(requests.get(image_input_url, stream=True).raw)
|
||||
image_prompt = Image.open(requests.get(image_prompt_url, stream=True).raw)
|
||||
mask_prompt = Image.open(requests.get(mask_prompt_url, stream=True).raw)
|
||||
|
||||
return image_input, image_prompt, mask_prompt
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convert_seggpt_checkpoint(args):
|
||||
model_name = args.model_name
|
||||
pytorch_dump_folder_path = args.pytorch_dump_folder_path
|
||||
verify_logits = args.verify_logits
|
||||
push_to_hub = args.push_to_hub
|
||||
|
||||
# Define default GroundingDINO configuation
|
||||
config = SegGptConfig()
|
||||
|
||||
# Load original checkpoint
|
||||
checkpoint_url = "https://huggingface.co/BAAI/SegGpt/blob/main/seggpt_vit_large.pth"
|
||||
original_state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"]
|
||||
|
||||
# # Rename keys
|
||||
new_state_dict = original_state_dict.copy()
|
||||
rename_keys = create_rename_keys(config)
|
||||
|
||||
for src, dest in rename_keys:
|
||||
rename_key(new_state_dict, src, dest)
|
||||
|
||||
# Load HF model
|
||||
model = SegGptForImageSegmentation(config)
|
||||
model.eval()
|
||||
missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
|
||||
print("Missing keys:", missing_keys)
|
||||
print("Unexpected keys:", unexpected_keys)
|
||||
|
||||
input_img, prompt_img, prompt_mask = prepare_input()
|
||||
image_processor = SegGptImageProcessor()
|
||||
inputs = image_processor(images=input_img, prompt_images=prompt_img, prompt_masks=prompt_mask, return_tensors="pt")
|
||||
|
||||
expected_prompt_pixel_values = torch.tensor(
|
||||
[
|
||||
[[-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965]],
|
||||
[[1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583]],
|
||||
[[2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088]],
|
||||
]
|
||||
)
|
||||
|
||||
expected_pixel_values = torch.tensor(
|
||||
[
|
||||
[[1.6324, 1.6153, 1.5810], [1.6153, 1.5982, 1.5810], [1.5810, 1.5639, 1.5639]],
|
||||
[[1.2731, 1.2556, 1.2206], [1.2556, 1.2381, 1.2031], [1.2206, 1.2031, 1.1681]],
|
||||
[[1.6465, 1.6465, 1.6465], [1.6465, 1.6465, 1.6465], [1.6291, 1.6291, 1.6291]],
|
||||
]
|
||||
)
|
||||
|
||||
expected_prompt_masks = torch.tensor(
|
||||
[
|
||||
[[-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179]],
|
||||
[[-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357]],
|
||||
[[-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044]],
|
||||
]
|
||||
)
|
||||
|
||||
assert torch.allclose(inputs.pixel_values[0, :, :3, :3], expected_pixel_values, atol=1e-4)
|
||||
assert torch.allclose(inputs.prompt_pixel_values[0, :, :3, :3], expected_prompt_pixel_values, atol=1e-4)
|
||||
assert torch.allclose(inputs.prompt_masks[0, :, :3, :3], expected_prompt_masks, atol=1e-4)
|
||||
|
||||
torch.manual_seed(2)
|
||||
outputs = model(**inputs)
|
||||
print(outputs)
|
||||
|
||||
if verify_logits:
|
||||
expected_output = torch.tensor(
|
||||
[
|
||||
[[-2.1208, -2.1190, -2.1198], [-2.1237, -2.1228, -2.1227], [-2.1232, -2.1226, -2.1228]],
|
||||
[[-2.0405, -2.0396, -2.0403], [-2.0434, -2.0434, -2.0433], [-2.0428, -2.0432, -2.0434]],
|
||||
[[-1.8102, -1.8088, -1.8099], [-1.8131, -1.8126, -1.8129], [-1.8130, -1.8128, -1.8131]],
|
||||
]
|
||||
)
|
||||
assert torch.allclose(outputs.pred_masks[0, :, :3, :3], expected_output, atol=1e-4)
|
||||
print("Looks good!")
|
||||
else:
|
||||
print("Converted without verifying logits")
|
||||
|
||||
if pytorch_dump_folder_path is not None:
|
||||
print(f"Saving model and processor for {model_name} to {pytorch_dump_folder_path}")
|
||||
model.save_pretrained(pytorch_dump_folder_path)
|
||||
image_processor.save_pretrained(pytorch_dump_folder_path)
|
||||
|
||||
if push_to_hub:
|
||||
print(f"Pushing model and processor for {model_name} to hub")
|
||||
model.push_to_hub(f"EduardoPacheco/{model_name}")
|
||||
image_processor.push_to_hub(f"EduardoPacheco/{model_name}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
# Required parameters
|
||||
parser.add_argument(
|
||||
"--model_name",
|
||||
default="seggpt-vit-large",
|
||||
type=str,
|
||||
choices=["seggpt-vit-large"],
|
||||
help="Name of the SegGpt model you'd like to convert.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verify_logits",
|
||||
action="store_false",
|
||||
help="Whether or not to verify the logits against the original implementation.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
convert_seggpt_checkpoint(args)
|
||||
626
src/transformers/models/seggpt/image_processing_seggpt.py
Normal file
626
src/transformers/models/seggpt/image_processing_seggpt.py
Normal file
@ -0,0 +1,626 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Image processor class for SegGPT."""
|
||||
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
|
||||
from ...image_transforms import resize, to_channel_dimension_format
|
||||
from ...image_utils import (
|
||||
IMAGENET_DEFAULT_MEAN,
|
||||
IMAGENET_DEFAULT_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
get_channel_dimension_axis,
|
||||
infer_channel_dimension_format,
|
||||
is_scaled_image,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
from ...utils import TensorType, is_torch_available, logging, requires_backends
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# See https://arxiv.org/pdf/2212.02499.pdf at 3.1 Redefining Output Spaces as "Images" - Semantic Segmentation from PAINTER paper
|
||||
# Taken from https://github.com/Abdullah-Meda/Painter/blob/main/Painter/data/coco_semseg/gen_color_coco_panoptic_segm.py#L31
|
||||
def build_palette(num_labels: int) -> List[Tuple[int, int]]:
|
||||
base = int(num_labels ** (1 / 3)) + 1
|
||||
margin = 256 // base
|
||||
|
||||
# we assume that class_idx 0 is the background which is mapped to black
|
||||
color_list = [(0, 0, 0)]
|
||||
for location in range(num_labels):
|
||||
num_seq_r = location // base**2
|
||||
num_seq_g = (location % base**2) // base
|
||||
num_seq_b = location % base
|
||||
|
||||
R = 255 - num_seq_r * margin
|
||||
G = 255 - num_seq_g * margin
|
||||
B = 255 - num_seq_b * margin
|
||||
|
||||
color_list.append((R, G, B))
|
||||
|
||||
return color_list
|
||||
|
||||
|
||||
def get_num_channels(image: np.ndarray, input_data_format: ChannelDimension) -> int:
|
||||
if image.ndim == 2:
|
||||
return 0
|
||||
|
||||
channel_idx = get_channel_dimension_axis(image, input_data_format)
|
||||
return image.shape[channel_idx]
|
||||
|
||||
|
||||
def mask_to_rgb(
|
||||
mask: np.ndarray,
|
||||
palette: Optional[List[Tuple[int, int]]] = None,
|
||||
input_data_format: Optional[ChannelDimension] = None,
|
||||
data_format: Optional[ChannelDimension] = None,
|
||||
) -> np.ndarray:
|
||||
if input_data_format is None and mask.ndim > 2:
|
||||
input_data_format = infer_channel_dimension_format(mask)
|
||||
|
||||
data_format = data_format if data_format is not None else input_data_format
|
||||
|
||||
num_channels = get_num_channels(mask, input_data_format)
|
||||
|
||||
if num_channels == 3:
|
||||
return to_channel_dimension_format(mask, data_format, input_data_format) if data_format is not None else mask
|
||||
|
||||
if palette is not None:
|
||||
height, width = mask.shape
|
||||
|
||||
rgb_mask = np.zeros((3, height, width), dtype=np.uint8)
|
||||
|
||||
classes_in_mask = np.unique(mask)
|
||||
|
||||
for class_idx in classes_in_mask:
|
||||
rgb_value = palette[class_idx]
|
||||
class_mask = (mask == class_idx).astype(np.uint8)
|
||||
class_mask = np.expand_dims(class_mask, axis=-1)
|
||||
class_rgb_mask = class_mask * np.array(rgb_value)
|
||||
class_rgb_mask = np.moveaxis(class_rgb_mask, -1, 0)
|
||||
rgb_mask += class_rgb_mask.astype(np.uint8)
|
||||
|
||||
rgb_mask = np.clip(rgb_mask, 0, 255).astype(np.uint8)
|
||||
|
||||
else:
|
||||
rgb_mask = np.repeat(mask[None, ...], 3, axis=0)
|
||||
|
||||
return (
|
||||
to_channel_dimension_format(rgb_mask, data_format, input_data_format) if data_format is not None else rgb_mask
|
||||
)
|
||||
|
||||
|
||||
class SegGptImageProcessor(BaseImageProcessor):
|
||||
r"""
|
||||
Constructs a SegGpt image processor.
|
||||
|
||||
Args:
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
|
||||
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
|
||||
size (`dict`, *optional*, defaults to `{"height": 448, "width": 448}`):
|
||||
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
|
||||
method.
|
||||
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
|
||||
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
|
||||
`preprocess` method.
|
||||
do_rescale (`bool`, *optional*, defaults to `True`):
|
||||
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
|
||||
parameter in the `preprocess` method.
|
||||
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
|
||||
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
|
||||
`preprocess` method.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
|
||||
method.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
|
||||
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
|
||||
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
|
||||
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
|
||||
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize: bool = True,
|
||||
size: Optional[Dict[str, int]] = None,
|
||||
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
||||
do_rescale: bool = True,
|
||||
rescale_factor: Union[int, float] = 1 / 255,
|
||||
do_normalize: bool = True,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__(**kwargs)
|
||||
size = size if size is not None else {"height": 448, "width": 448}
|
||||
size = get_size_dict(size)
|
||||
self.do_resize = do_resize
|
||||
self.do_rescale = do_rescale
|
||||
self.do_normalize = do_normalize
|
||||
self.size = size
|
||||
self.resample = resample
|
||||
self.rescale_factor = rescale_factor
|
||||
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
|
||||
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
|
||||
|
||||
def get_palette(self, num_labels: int) -> List[Tuple[int, int]]:
|
||||
"""Build a palette to map the prompt mask from a single channel to a 3 channel RGB.
|
||||
|
||||
Args:
|
||||
num_labels (`int`):
|
||||
Number of classes in the segmentation task (excluding the background).
|
||||
|
||||
Returns:
|
||||
`List[Tuple[int, int]]`: Palette to map the prompt mask from a single channel to a 3 channel RGB.
|
||||
"""
|
||||
return build_palette(num_labels)
|
||||
|
||||
def mask_to_rgb(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
palette: Optional[List[Tuple[int, int]]] = None,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
) -> np.ndarray:
|
||||
"""Convert a mask to RGB format.
|
||||
|
||||
Args:
|
||||
image (`np.ndarray`):
|
||||
Mask to convert to RGB format. If the mask is already in RGB format, it will be passed through.
|
||||
palette (`List[Tuple[int, int]]`, *optional*, defaults to `None`):
|
||||
Palette to use to convert the mask to RGB format. If unset, the mask is duplicated across the channel
|
||||
dimension.
|
||||
data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the output image. If unset, the channel dimension format of the input
|
||||
image is used. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
||||
from the input image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
|
||||
Returns:
|
||||
`np.ndarray`: The mask in RGB format.
|
||||
"""
|
||||
return mask_to_rgb(
|
||||
image,
|
||||
palette=palette,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
|
||||
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
|
||||
def resize(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
size: Dict[str, int],
|
||||
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Resize an image to `(size["height"], size["width"])`.
|
||||
|
||||
Args:
|
||||
image (`np.ndarray`):
|
||||
Image to resize.
|
||||
size (`Dict[str, int]`):
|
||||
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
|
||||
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
|
||||
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
|
||||
data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the output image. If unset, the channel dimension format of the input
|
||||
image is used. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
||||
from the input image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
||||
|
||||
Returns:
|
||||
`np.ndarray`: The resized image.
|
||||
"""
|
||||
size = get_size_dict(size)
|
||||
if "height" not in size or "width" not in size:
|
||||
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
|
||||
output_size = (size["height"], size["width"])
|
||||
return resize(
|
||||
image,
|
||||
size=output_size,
|
||||
resample=resample,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _preprocess_step(
|
||||
self,
|
||||
images: ImageInput,
|
||||
is_mask: bool = False,
|
||||
do_resize: Optional[bool] = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_rescale: Optional[bool] = None,
|
||||
rescale_factor: Optional[float] = None,
|
||||
do_normalize: Optional[bool] = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
num_labels: Optional[int] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Preprocess an image or batch of images.
|
||||
|
||||
Args:
|
||||
images (`ImageInput`):
|
||||
Image to _preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
||||
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
||||
is_mask (`bool`, *optional*, defaults to `False`):
|
||||
Whether the image is a mask. If True, the image is converted to RGB using the palette if
|
||||
`self.num_labels` is specified otherwise RGB is achieved by duplicating the channel.
|
||||
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
||||
Whether to resize the image.
|
||||
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
||||
Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
|
||||
resizing.
|
||||
resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
|
||||
`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BICUBIC`. Only has
|
||||
an effect if `do_resize` is set to `True`.
|
||||
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
||||
Whether to rescale the image values between [0 - 1].
|
||||
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
||||
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
||||
Whether to normalize the image.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
||||
Image mean to use if `do_normalize` is set to `True`.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
||||
Image standard deviation to use if `do_normalize` is set to `True`.
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
The type of tensors to return. Can be one of:
|
||||
- Unset: Return a list of `np.ndarray`.
|
||||
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
|
||||
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
|
||||
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
|
||||
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
|
||||
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
||||
The channel dimension format for the output image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- Unset: Use the channel dimension format of the input image.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
||||
from the input image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
||||
num_labels: (`int`, *optional*):
|
||||
Number of classes in the segmentation task (excluding the background). If specified, a palette will be
|
||||
built, assuming that class_idx 0 is the background, to map the prompt mask from a single class_idx
|
||||
channel to a 3 channel RGB. Not specifying this will result in the prompt mask either being passed
|
||||
through as is if it is already in RGB format or being duplicated across the channel dimension.
|
||||
"""
|
||||
do_resize = do_resize if do_resize is not None else self.do_resize
|
||||
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
|
||||
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
|
||||
resample = resample if resample is not None else self.resample
|
||||
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
|
||||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
size = size if size is not None else self.size
|
||||
size_dict = get_size_dict(size)
|
||||
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
||||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
if do_resize and size is None:
|
||||
raise ValueError("Size must be specified if do_resize is True.")
|
||||
|
||||
if do_rescale and rescale_factor is None:
|
||||
raise ValueError("Rescale factor must be specified if do_rescale is True.")
|
||||
|
||||
if do_normalize and (image_mean is None or image_std is None):
|
||||
raise ValueError("Image mean and std must be specified if do_normalize is True.")
|
||||
|
||||
# All transformations expect numpy arrays.
|
||||
images = [to_numpy_array(image) for image in images]
|
||||
|
||||
if is_scaled_image(images[0]) and do_rescale:
|
||||
logger.warning_once(
|
||||
"It looks like you are trying to rescale already rescaled images. If the input"
|
||||
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
|
||||
)
|
||||
|
||||
if input_data_format is None and not is_mask:
|
||||
# We assume that all images have the same channel dimension format.
|
||||
input_data_format = infer_channel_dimension_format(images[0])
|
||||
|
||||
if is_mask:
|
||||
palette = self.get_palette(num_labels) if num_labels is not None else None
|
||||
# Since this is the input for the next transformations its format should be the same as the input_data_format
|
||||
images = [
|
||||
self.mask_to_rgb(image=image, palette=palette, data_format=ChannelDimension.FIRST) for image in images
|
||||
]
|
||||
input_data_format = ChannelDimension.FIRST
|
||||
|
||||
if do_resize:
|
||||
images = [
|
||||
self.resize(image=image, size=size_dict, resample=resample, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_rescale:
|
||||
images = [
|
||||
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_normalize:
|
||||
images = [
|
||||
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
images = [
|
||||
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
|
||||
]
|
||||
|
||||
return images
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: Optional[ImageInput] = None,
|
||||
prompt_images: Optional[ImageInput] = None,
|
||||
prompt_masks: Optional[ImageInput] = None,
|
||||
do_resize: Optional[bool] = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_rescale: Optional[bool] = None,
|
||||
rescale_factor: Optional[float] = None,
|
||||
do_normalize: Optional[bool] = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
num_labels: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Preprocess an image or batch of images.
|
||||
|
||||
Args:
|
||||
images (`ImageInput`):
|
||||
Image to _preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
||||
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
||||
prompt_images (`ImageInput`):
|
||||
Prompt image to _preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
||||
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
||||
prompt_masks (`ImageInput`):
|
||||
Prompt mask from prompt image to _preprocess. Expects a single or batch of masks. If the mask masks are
|
||||
a single channel then it will be converted to RGB using the palette if `self.num_labels` is specified
|
||||
or by just repeating the channel if not. If the mask is already in RGB format, it will be passed through.
|
||||
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
||||
Whether to resize the image.
|
||||
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
||||
Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
|
||||
resizing.
|
||||
resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
|
||||
`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BICUBIC`. Only has
|
||||
an effect if `do_resize` is set to `True`. Doesn't apply to prompt mask as it is resized using nearest.
|
||||
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
||||
Whether to rescale the image values between [0 - 1].
|
||||
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
||||
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
||||
Whether to normalize the image.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
||||
Image mean to use if `do_normalize` is set to `True`.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
||||
Image standard deviation to use if `do_normalize` is set to `True`.
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
The type of tensors to return. Can be one of:
|
||||
- Unset: Return a list of `np.ndarray`.
|
||||
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
|
||||
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
|
||||
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
|
||||
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
|
||||
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
||||
The channel dimension format for the output image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- Unset: Use the channel dimension format of the input image.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
||||
from the input image. Can be one of:
|
||||
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
||||
num_labels: (`int`, *optional*):
|
||||
Number of classes in the segmentation task (excluding the background). If specified, a palette will be
|
||||
built, assuming that class_idx 0 is the background, to map the prompt mask from a single class_idx
|
||||
channel to a 3 channel RGB. Not specifying this will result in the prompt mask either being passed
|
||||
through as is if it is already in RGB format or being duplicated across the channel dimension.
|
||||
"""
|
||||
if all(v is None for v in [images, prompt_images, prompt_masks]):
|
||||
raise ValueError("At least one of images, prompt_images, prompt_masks must be specified.")
|
||||
|
||||
data = {}
|
||||
|
||||
if images is not None:
|
||||
images = self._preprocess_step(
|
||||
images,
|
||||
is_mask=False,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
do_rescale=do_rescale,
|
||||
rescale_factor=rescale_factor,
|
||||
do_normalize=do_normalize,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
data["pixel_values"] = images
|
||||
|
||||
if prompt_images is not None:
|
||||
prompt_images = self._preprocess_step(
|
||||
prompt_images,
|
||||
is_mask=False,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
do_rescale=do_rescale,
|
||||
rescale_factor=rescale_factor,
|
||||
do_normalize=do_normalize,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
data["prompt_pixel_values"] = prompt_images
|
||||
|
||||
if prompt_masks is not None:
|
||||
prompt_masks = self._preprocess_step(
|
||||
prompt_masks,
|
||||
is_mask=True,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=PILImageResampling.NEAREST,
|
||||
do_rescale=do_rescale,
|
||||
rescale_factor=rescale_factor,
|
||||
do_normalize=do_normalize,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
num_labels=num_labels,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
data["prompt_masks"] = prompt_masks
|
||||
|
||||
return BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
def post_process_semantic_segmentation(
|
||||
self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None, num_labels: Optional[int] = None
|
||||
):
|
||||
"""
|
||||
Converts the output of [`SegGptImageSegmentationOutput`] into segmentation maps. Only supports
|
||||
PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`SegGptImageSegmentationOutput`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`List[Tuple[int, int]]`, *optional*):
|
||||
List of length (batch_size), where each list item (`Tuple[int, int]`) corresponds to the requested
|
||||
final size (height, width) of each prediction. If left to None, predictions will not be resized.
|
||||
num_labels (`int`, *optional*):
|
||||
Number of classes in the segmentation task (excluding the background). If specified, a palette will be
|
||||
built, assuming that class_idx 0 is the background, to map prediction masks from RGB values to class
|
||||
indices. This value should be the same used when preprocessing inputs.
|
||||
Returns:
|
||||
semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
|
||||
segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
|
||||
specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
|
||||
"""
|
||||
requires_backends(self, ["torch"])
|
||||
# batch_size x num_channels x 2*height x width
|
||||
masks = outputs.pred_masks
|
||||
|
||||
# Predicted mask and prompt are concatenated in the height dimension
|
||||
# batch_size x num_channels x height x width
|
||||
masks = masks[:, :, masks.shape[2] // 2 :, :]
|
||||
|
||||
# To unnormalize we need to permute to channel last
|
||||
# batch_size x height x width x num_channels
|
||||
std = torch.tensor(self.image_std).to(masks.device)
|
||||
mean = torch.tensor(self.image_mean).to(masks.device)
|
||||
|
||||
masks = masks.permute(0, 2, 3, 1) * std + mean
|
||||
|
||||
# batch_size x num_channels x height x width
|
||||
masks = masks.permute(0, 3, 1, 2)
|
||||
|
||||
# Clip to match with palette if specified
|
||||
masks = torch.clip(masks * 255, 0, 255)
|
||||
|
||||
semantic_segmentation = []
|
||||
palette_tensor = None
|
||||
palette = self.get_palette(num_labels) if num_labels is not None else None
|
||||
if palette is not None:
|
||||
palette_tensor = torch.tensor(palette).float().to(masks.device)
|
||||
_, num_channels, _, _ = masks.shape
|
||||
palette_tensor = palette_tensor.view(1, 1, num_labels + 1, num_channels)
|
||||
|
||||
for idx, mask in enumerate(masks):
|
||||
if target_sizes is not None:
|
||||
mask = torch.nn.functional.interpolate(
|
||||
mask.unsqueeze(0),
|
||||
size=target_sizes[idx],
|
||||
mode="nearest",
|
||||
)[0]
|
||||
|
||||
if num_labels is not None:
|
||||
channels, height, width = mask.shape
|
||||
dist = mask.permute(1, 2, 0).view(height, width, 1, channels)
|
||||
dist = dist - palette_tensor
|
||||
dist = torch.pow(dist, 2)
|
||||
dist = torch.sum(dist, dim=-1)
|
||||
pred = dist.argmin(dim=-1)
|
||||
|
||||
else:
|
||||
# If no palette is specified SegGpt will try to paint using the mask class idx as RGB
|
||||
pred = mask.mean(dim=0).int()
|
||||
|
||||
semantic_segmentation.append(pred)
|
||||
|
||||
return semantic_segmentation
|
||||
1014
src/transformers/models/seggpt/modeling_seggpt.py
Normal file
1014
src/transformers/models/seggpt/modeling_seggpt.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -7556,6 +7556,30 @@ class SegformerPreTrainedModel(metaclass=DummyObject):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
class SegGptForImageSegmentation(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SegGptModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SegGptPreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
SEW_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
|
||||
@ -471,6 +471,13 @@ class SegformerImageProcessor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class SegGptImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class SiglipImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
|
||||
0
tests/models/seggpt/__init__.py
Normal file
0
tests/models/seggpt/__init__.py
Normal file
231
tests/models/seggpt/test_image_processing_seggpt.py
Normal file
231
tests/models/seggpt/test_image_processing_seggpt.py
Normal file
@ -0,0 +1,231 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 HuggingFace Inc.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
from datasets import load_dataset
|
||||
|
||||
from transformers.testing_utils import require_torch, require_vision, slow
|
||||
from transformers.utils import is_torch_available, is_vision_available
|
||||
|
||||
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers.models.seggpt.modeling_seggpt import SegGptImageSegmentationOutput
|
||||
|
||||
if is_vision_available():
|
||||
from transformers import SegGptImageProcessor
|
||||
|
||||
|
||||
class SegGptImageProcessingTester(unittest.TestCase):
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=7,
|
||||
num_channels=3,
|
||||
image_size=18,
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
):
|
||||
size = size if size is not None else {"height": 18, "width": 18}
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
self.image_size = image_size
|
||||
self.min_resolution = min_resolution
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_normalize": self.do_normalize,
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
}
|
||||
|
||||
def expected_output_image_shape(self, images):
|
||||
return self.num_channels, self.size["height"], self.size["width"]
|
||||
|
||||
def expected_post_processed_shape(self):
|
||||
return self.size["height"] // 2, self.size["width"]
|
||||
|
||||
def get_fake_image_segmentation_output(self):
|
||||
torch.manual_seed(42)
|
||||
return SegGptImageSegmentationOutput(
|
||||
pred_masks=torch.rand(self.batch_size, self.num_channels, self.size["height"], self.size["width"])
|
||||
)
|
||||
|
||||
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
|
||||
return prepare_image_inputs(
|
||||
batch_size=self.batch_size,
|
||||
num_channels=self.num_channels,
|
||||
min_resolution=self.min_resolution,
|
||||
max_resolution=self.max_resolution,
|
||||
equal_resolution=equal_resolution,
|
||||
numpify=numpify,
|
||||
torchify=torchify,
|
||||
)
|
||||
|
||||
|
||||
def prepare_mask():
|
||||
ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"]
|
||||
return ds[0]["mask"].convert("L")
|
||||
|
||||
|
||||
def prepare_img():
|
||||
ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"]
|
||||
images = [image.convert("RGB") for image in ds["image"]]
|
||||
masks = [image.convert("RGB") for image in ds["mask"]]
|
||||
return images, masks
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_vision
|
||||
class SegGptImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
image_processing_class = SegGptImageProcessor if is_vision_available() else None
|
||||
|
||||
def setUp(self):
|
||||
self.image_processor_tester = SegGptImageProcessingTester(self)
|
||||
|
||||
@property
|
||||
def image_processor_dict(self):
|
||||
return self.image_processor_tester.prepare_image_processor_dict()
|
||||
|
||||
def test_image_processor_properties(self):
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
self.assertTrue(hasattr(image_processing, "image_mean"))
|
||||
self.assertTrue(hasattr(image_processing, "image_std"))
|
||||
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
||||
self.assertTrue(hasattr(image_processing, "do_resize"))
|
||||
self.assertTrue(hasattr(image_processing, "size"))
|
||||
|
||||
def test_image_processor_from_dict_with_kwargs(self):
|
||||
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
|
||||
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
|
||||
|
||||
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
|
||||
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
|
||||
|
||||
def test_image_processor_palette(self):
|
||||
num_labels = 3
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
palette = image_processing.get_palette(num_labels)
|
||||
self.assertEqual(len(palette), num_labels + 1)
|
||||
self.assertEqual(palette[0], (0, 0, 0))
|
||||
|
||||
def test_mask_equivalence(self):
|
||||
image_processor = SegGptImageProcessor()
|
||||
|
||||
mask_binary = prepare_mask()
|
||||
mask_rgb = mask_binary.convert("RGB")
|
||||
|
||||
inputs_binary = image_processor(images=None, prompt_masks=mask_binary, return_tensors="pt")
|
||||
inputs_rgb = image_processor(images=None, prompt_masks=mask_rgb, return_tensors="pt")
|
||||
|
||||
self.assertTrue((inputs_binary["prompt_masks"] == inputs_rgb["prompt_masks"]).all().item())
|
||||
|
||||
def test_mask_to_rgb(self):
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
mask = prepare_mask()
|
||||
mask = np.array(mask)
|
||||
mask = (mask > 0).astype(np.uint8)
|
||||
|
||||
def check_two_colors(image, color1=(0, 0, 0), color2=(255, 255, 255)):
|
||||
pixels = image.transpose(1, 2, 0).reshape(-1, 3)
|
||||
unique_colors = np.unique(pixels, axis=0)
|
||||
if len(unique_colors) == 2 and (color1 in unique_colors) and (color2 in unique_colors):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
num_labels = 1
|
||||
palette = image_processing.get_palette(num_labels)
|
||||
|
||||
# Should only duplicate repeat class indices map, hence only (0,0,0) and (1,1,1)
|
||||
mask_duplicated = image_processing.mask_to_rgb(mask)
|
||||
# Mask using palette, since only 1 class is present we have colors (0,0,0) and (255,255,255)
|
||||
mask_painted = image_processing.mask_to_rgb(mask, palette=palette)
|
||||
|
||||
self.assertTrue(check_two_colors(mask_duplicated, color2=(1, 1, 1)))
|
||||
self.assertTrue(check_two_colors(mask_painted, color2=(255, 255, 255)))
|
||||
|
||||
def test_post_processing_semantic_segmentation(self):
|
||||
image_processor = self.image_processing_class(**self.image_processor_dict)
|
||||
outputs = self.image_processor_tester.get_fake_image_segmentation_output()
|
||||
post_processed = image_processor.post_process_semantic_segmentation(outputs)
|
||||
|
||||
self.assertEqual(len(post_processed), self.image_processor_tester.batch_size)
|
||||
|
||||
expected_semantic_map_shape = self.image_processor_tester.expected_post_processed_shape()
|
||||
self.assertEqual(post_processed[0].shape, expected_semantic_map_shape)
|
||||
|
||||
@slow
|
||||
def test_pixel_values(self):
|
||||
images, masks = prepare_img()
|
||||
input_image = images[1]
|
||||
prompt_image = images[0]
|
||||
prompt_mask = masks[0]
|
||||
|
||||
image_processor = SegGptImageProcessor.from_pretrained("BAAI/seggpt-vit-large")
|
||||
|
||||
inputs = image_processor(
|
||||
images=input_image, prompt_images=prompt_image, prompt_masks=prompt_mask, return_tensors="pt"
|
||||
)
|
||||
|
||||
# Verify pixel values
|
||||
expected_prompt_pixel_values = torch.tensor(
|
||||
[
|
||||
[[-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965]],
|
||||
[[1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583]],
|
||||
[[2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088]],
|
||||
]
|
||||
)
|
||||
|
||||
expected_pixel_values = torch.tensor(
|
||||
[
|
||||
[[1.6324, 1.6153, 1.5810], [1.6153, 1.5982, 1.5810], [1.5810, 1.5639, 1.5639]],
|
||||
[[1.2731, 1.2556, 1.2206], [1.2556, 1.2381, 1.2031], [1.2206, 1.2031, 1.1681]],
|
||||
[[1.6465, 1.6465, 1.6465], [1.6465, 1.6465, 1.6465], [1.6291, 1.6291, 1.6291]],
|
||||
]
|
||||
)
|
||||
|
||||
expected_prompt_masks = torch.tensor(
|
||||
[
|
||||
[[-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179]],
|
||||
[[-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357]],
|
||||
[[-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044]],
|
||||
]
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(inputs.pixel_values[0, :, :3, :3], expected_pixel_values, atol=1e-4))
|
||||
self.assertTrue(
|
||||
torch.allclose(inputs.prompt_pixel_values[0, :, :3, :3], expected_prompt_pixel_values, atol=1e-4)
|
||||
)
|
||||
self.assertTrue(torch.allclose(inputs.prompt_masks[0, :, :3, :3], expected_prompt_masks, atol=1e-4))
|
||||
339
tests/models/seggpt/test_modeling_seggpt.py
Normal file
339
tests/models/seggpt/test_modeling_seggpt.py
Normal file
@ -0,0 +1,339 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Testing suite for the PyTorch SegGpt model. """
|
||||
|
||||
|
||||
import inspect
|
||||
import unittest
|
||||
|
||||
from datasets import load_dataset
|
||||
|
||||
from transformers import SegGptConfig
|
||||
from transformers.testing_utils import (
|
||||
require_torch,
|
||||
require_vision,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
from transformers.utils import cached_property, is_torch_available, is_vision_available
|
||||
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, floats_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from transformers import SegGptForImageSegmentation, SegGptModel
|
||||
from transformers.models.seggpt.modeling_seggpt import SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from transformers import SegGptImageProcessor
|
||||
|
||||
|
||||
class SegGptModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=2,
|
||||
image_size=30,
|
||||
patch_size=2,
|
||||
num_channels=3,
|
||||
is_training=False,
|
||||
use_labels=True,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout_prob=0.1,
|
||||
attention_probs_dropout_prob=0.1,
|
||||
initializer_range=0.02,
|
||||
mlp_ratio=2.0,
|
||||
merge_index=0,
|
||||
intermediate_hidden_state_indices=[1],
|
||||
pretrain_image_size=10,
|
||||
decoder_hidden_size=10,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.num_channels = num_channels
|
||||
self.is_training = is_training
|
||||
self.use_labels = use_labels
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||||
self.initializer_range = initializer_range
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.merge_index = merge_index
|
||||
self.intermediate_hidden_state_indices = intermediate_hidden_state_indices
|
||||
self.pretrain_image_size = pretrain_image_size
|
||||
self.decoder_hidden_size = decoder_hidden_size
|
||||
|
||||
# in SegGpt, the seq length equals the number of patches (we don't use the [CLS] token)
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
self.seq_length = num_patches
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size // 2, self.image_size])
|
||||
prompt_pixel_values = floats_tensor(
|
||||
[self.batch_size, self.num_channels, self.image_size // 2, self.image_size]
|
||||
)
|
||||
prompt_masks = floats_tensor([self.batch_size, self.num_channels, self.image_size // 2, self.image_size])
|
||||
|
||||
labels = None
|
||||
if self.use_labels:
|
||||
labels = floats_tensor([self.batch_size, self.num_channels, self.image_size // 2, self.image_size])
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, pixel_values, prompt_pixel_values, prompt_masks, labels
|
||||
|
||||
def get_config(self):
|
||||
return SegGptConfig(
|
||||
image_size=self.image_size,
|
||||
patch_size=self.patch_size,
|
||||
num_channels=self.num_channels,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
hidden_act=self.hidden_act,
|
||||
hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
initializer_range=self.initializer_range,
|
||||
mlp_ratio=self.mlp_ratio,
|
||||
merge_index=self.merge_index,
|
||||
intermediate_hidden_state_indices=self.intermediate_hidden_state_indices,
|
||||
pretrain_image_size=self.pretrain_image_size,
|
||||
decoder_hidden_size=self.decoder_hidden_size,
|
||||
)
|
||||
|
||||
def create_and_check_model(self, config, pixel_values, prompt_pixel_values, prompt_masks, labels):
|
||||
model = SegGptModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(pixel_values, prompt_pixel_values, prompt_masks)
|
||||
self.parent.assertEqual(
|
||||
result.last_hidden_state.shape,
|
||||
(
|
||||
self.batch_size,
|
||||
self.image_size // self.patch_size,
|
||||
self.image_size // self.patch_size,
|
||||
self.hidden_size,
|
||||
),
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
pixel_values,
|
||||
prompt_pixel_values,
|
||||
prompt_masks,
|
||||
labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {
|
||||
"pixel_values": pixel_values,
|
||||
"prompt_pixel_values": prompt_pixel_values,
|
||||
"prompt_masks": prompt_masks,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class SegGptModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
"""
|
||||
Here we also overwrite some of the tests of test_modeling_common.py, as SegGpt does not use input_ids, inputs_embeds,
|
||||
attention_mask and seq_length.
|
||||
"""
|
||||
|
||||
all_model_classes = (SegGptModel, SegGptForImageSegmentation) if is_torch_available() else ()
|
||||
fx_compatible = False
|
||||
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_head_masking = False
|
||||
test_torchscript = False
|
||||
pipeline_model_mapping = (
|
||||
{"feature-extraction": SegGptModel, "mask-generation": SegGptModel} if is_torch_available() else {}
|
||||
)
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = SegGptModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=SegGptConfig, has_text_modality=False)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
@unittest.skip(reason="SegGpt does not use inputs_embeds")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
def test_model_common_attributes(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
|
||||
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
signature = inspect.signature(model.forward)
|
||||
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||
arg_names = [*signature.parameters.keys()]
|
||||
|
||||
expected_arg_names = ["pixel_values", "prompt_pixel_values", "prompt_masks"]
|
||||
self.assertListEqual(arg_names[:3], expected_arg_names)
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_hidden_states_output(self):
|
||||
def check_hidden_states_output(inputs_dict, config, model_class):
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
||||
|
||||
expected_num_layers = getattr(
|
||||
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
||||
)
|
||||
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||
|
||||
patch_height = patch_width = config.image_size // config.patch_size
|
||||
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-3:]),
|
||||
[patch_height, patch_width, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
# check that output_hidden_states also work using config
|
||||
del inputs_dict["output_hidden_states"]
|
||||
config.output_hidden_states = True
|
||||
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = SegGptModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
def prepare_img():
|
||||
ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"]
|
||||
images = [image.convert("RGB") for image in ds["image"]]
|
||||
masks = [image.convert("RGB") for image in ds["mask"]]
|
||||
return images, masks
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_vision
|
||||
class SegGptModelIntegrationTest(unittest.TestCase):
|
||||
@cached_property
|
||||
def default_image_processor(self):
|
||||
return SegGptImageProcessor.from_pretrained("BAAI/seggpt-vit-large") if is_vision_available() else None
|
||||
|
||||
@slow
|
||||
def test_one_shot_inference(self):
|
||||
model = SegGptForImageSegmentation.from_pretrained("BAAI/seggpt-vit-large").to(torch_device)
|
||||
|
||||
image_processor = self.default_image_processor
|
||||
|
||||
images, masks = prepare_img()
|
||||
input_image = images[1]
|
||||
prompt_image = images[0]
|
||||
prompt_mask = masks[0]
|
||||
|
||||
inputs = image_processor(
|
||||
images=input_image, prompt_images=prompt_image, prompt_masks=prompt_mask, return_tensors="pt"
|
||||
)
|
||||
|
||||
inputs = inputs.to(torch_device)
|
||||
# forward pass
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
# verify the logits
|
||||
expected_shape = torch.Size((1, 3, 896, 448))
|
||||
self.assertEqual(outputs.pred_masks.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor(
|
||||
[
|
||||
[[-2.1208, -2.1190, -2.1198], [-2.1237, -2.1228, -2.1227], [-2.1232, -2.1226, -2.1228]],
|
||||
[[-2.0405, -2.0396, -2.0403], [-2.0434, -2.0434, -2.0433], [-2.0428, -2.0432, -2.0434]],
|
||||
[[-1.8102, -1.8088, -1.8099], [-1.8131, -1.8126, -1.8129], [-1.8130, -1.8128, -1.8131]],
|
||||
]
|
||||
).to(torch_device)
|
||||
|
||||
self.assertTrue(torch.allclose(outputs.pred_masks[0, :, :3, :3], expected_slice, atol=1e-4))
|
||||
|
||||
result = image_processor.post_process_semantic_segmentation(outputs, [input_image.size[::-1]])[0]
|
||||
|
||||
result_expected_shape = torch.Size((170, 297))
|
||||
expected_area = 1082
|
||||
area = (result > 0).sum().item()
|
||||
self.assertEqual(result.shape, result_expected_shape)
|
||||
self.assertEqual(area, expected_area)
|
||||
|
||||
@slow
|
||||
def test_few_shot_inference(self):
|
||||
model = SegGptForImageSegmentation.from_pretrained("BAAI/seggpt-vit-large").to(torch_device)
|
||||
image_processor = self.default_image_processor
|
||||
|
||||
images, masks = prepare_img()
|
||||
input_images = [images[1]] * 2
|
||||
prompt_images = [images[0], images[2]]
|
||||
prompt_masks = [masks[0], masks[2]]
|
||||
|
||||
inputs = image_processor(
|
||||
images=input_images, prompt_images=prompt_images, prompt_masks=prompt_masks, return_tensors="pt"
|
||||
)
|
||||
|
||||
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs, feature_ensemble=True)
|
||||
|
||||
expected_shape = torch.Size((2, 3, 896, 448))
|
||||
expected_slice = torch.tensor(
|
||||
[
|
||||
[[-2.1201, -2.1192, -2.1189], [-2.1217, -2.1210, -2.1204], [-2.1216, -2.1202, -2.1194]],
|
||||
[[-2.0393, -2.0390, -2.0387], [-2.0402, -2.0402, -2.0397], [-2.0400, -2.0394, -2.0388]],
|
||||
[[-1.8083, -1.8076, -1.8077], [-1.8105, -1.8102, -1.8099], [-1.8105, -1.8095, -1.8090]],
|
||||
]
|
||||
).to(torch_device)
|
||||
|
||||
self.assertEqual(outputs.pred_masks.shape, expected_shape)
|
||||
self.assertTrue(torch.allclose(outputs.pred_masks[0, :, 448:451, :3], expected_slice, atol=4e-4))
|
||||
@ -958,6 +958,16 @@ class ModelTesterMixin:
|
||||
traced_model = torch.jit.trace(
|
||||
model, (input_ids, bbox), check_trace=False
|
||||
) # when traced model is checked, an error is produced due to name mangling
|
||||
elif (
|
||||
"pixel_values" in inputs and "prompt_pixel_values" in inputs and "prompt_masks" in inputs
|
||||
): # SegGpt requires additional inputs
|
||||
pixel_values = inputs["pixel_values"]
|
||||
prompt_pixel_values = inputs["prompt_pixel_values"]
|
||||
prompt_masks = inputs["prompt_masks"]
|
||||
model(pixel_values, prompt_pixel_values, prompt_masks)
|
||||
traced_model = torch.jit.trace(
|
||||
model, (pixel_values, prompt_pixel_values, prompt_masks), check_trace=False
|
||||
) # when traced model is checked, an error is produced due to name mangling
|
||||
else:
|
||||
main_input = inputs[main_input_name]
|
||||
|
||||
|
||||
@ -308,6 +308,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
|
||||
"SeamlessM4Tv2NARTextToUnitForConditionalGeneration",
|
||||
"SeamlessM4Tv2CodeHifiGan",
|
||||
"SeamlessM4Tv2ForSpeechToSpeech", # no auto class for speech-to-speech
|
||||
"SegGptForImageSegmentation",
|
||||
"SiglipVisionModel",
|
||||
"SiglipTextModel",
|
||||
]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user